Identifying the core bacterial microbiome of hydrocarbon degradation and a shift of dominant methanogenesis pathways in the oil and aqueous phases of petroleum reservoirs of different temperatures from China

Identifying the core bacterial microbiome of hydrocarbon degradation and a shift of dominant methanogenesis pathways in the oil and aqueous phases of petroleum reservoirs of different temperatures from China

Identifying the core bacterial microbiome of hydrocarbon degradation and a shift of dominant methanogenesis pathways in the oil and aqueous phases of petroleum reservoirs of different temperatures from ChinaIdentifying the core bacterial microbiome of hydrocarbon degradation and a shift of dominant...Zhichao Zhou et al.

Microorganisms in petroleum reservoirs play significant roles in hydrocarbon
degradation, and through the terminal electron-accepting process of
methanogenesis, they also contribute to microbially enhanced oil recovery
(MEOR) worldwide, with great economic and environmental benefits. Here, a
molecular investigation, using the 16S rRNA and mcrA gene profiles based on
MiSeq sequencing and clone library construction methods, was conducted on
oil and water (aqueous) phases of samples of high (82–88 ∘C),
moderate (45–63 ∘C), and low temperatures (21–32 ∘C) from
seven petroleum reservoirs in China. A core bacterial microbiome with a
small proportion of shared operational taxonomic unit (OTU) values, but a high
proportion of sequences among all reservoirs was discovered, including
aerobic degraders, sulfate- and nitrate-reducing bacteria, fermentative bacteria,
and sulfur-oxidizing bacteria distributed mainly in Proteobacteria, Bacteroidetes, Deferribacteres,
Deinococcus–Thermus, Firmicutes, Spirochaetes, and Thermotogae. Their prevalence in the previously reported petroleum reservoirs
and successive enrichment cultures suggests their common roles and functions
involved in aliphatic and aromatic hydrocarbon degradation. The methanogenic
process generally shifts from the dominant hydrogenotrophic pathway in the
aqueous phase to the acetoclastic pathway in the oil phase in high-temperature
reservoirs, but the opposite was true for low-temperature reservoirs. No
difference was detected between the two phases in moderate temperature
reservoirs. Physicochemical factors, including pH; temperature; phase
conditions; and nitrate, Mn2+, and Mg2+ concentrations were the
main factors correlated to the microbial compositional and functional profiles
significantly. Linear discriminant analysis (LDA) effect size (LEfSe) analysis shows distribution
differences of microbial groups towards pH, temperature, and the oil and aqueous
phases. Using the software Tax4Fun for functional profiling indicated
functional metabolism differences between the two phases, including amino
acids, hydrocarbons in the oil phase, and carbohydrates in the aqueous phase.

Petroleum reservoir is a complex system consisting of porous sandstones with
oil, water, and air. Microorganisms attached to the oil phase of petroleum
fluids are largely neglected in majority of the previous investigations due
to technical difficulties in DNA extraction and sequencing (Kryachko et al.,
2012). Oil-attached microorganisms influence the oil–water interface
properties via the production of biosurfactants and metabolites or the formation of adsorbed colloidal particles to enhance oil recovery performance
(Kobayashi et al., 2012; Kryachko et al., 2012; Wang et al., 2014). The
distribution, function, and contribution of these microorganisms to MEOR
success could be very different from those of the aqueous phase (Kobayashi
et al., 2012). Oil-degrading microorganisms, including thermophilic
hydrocarbon-degrading archaea and methanogens, play different roles in the
MEOR process (Mbadinga et al., 2011). Their compositional
patterns and functional profiles in terms of temperature and the oil and aqueous phases
are of great value for the understanding of the mechanism of MEOR. Knowledge of the
major microbial drivers, their potential functions, distribution
characteristics, and changing patterns towards environmental parameters
should be one of the research directions taken for a better understanding of the
MEOR process. In this study, the research objectives were to reveal the
compositional and functional differences of petroleum-reservoir-inhabiting
microorganisms under different temperatures (high: 82–88 ∘C;
moderate: 45–63 ∘C; and low: 21–32 ∘C), the
methanogenesis pathways in the oil and aqueous phases of these samples, and the
influence of physicochemical factors on microbial community composition.

2.1 Characterization of geographic properties of sampling reservoirs

Petroleum production fluid samples were collected from seven oilfields
across China, covering oil wells of different geographical locations and
temperatures. The reservoirs and crude oil properties, together with the
the aqueous-phase chemical characteristics of this study, are listed in Table 1.
Detailed reservoir properties are described in the Supplement.

Each sample, containing a mixture of crude oil and water, was collected on
site in a sterilized container after flushing each wellhead for at least
3–5 min. The containers were screw capped to avoid air invasion and
transported back to the nearby laboratory immediately for further
processing. The oil and water mixture was gently heated to 50 ∘C
to make it semifluid and then separated into oil and aqueous phases in a
separatory funnel. Heating was operated as quickly as possible according to
the solidification degree of individual petroleum fluid samples, in order to
reduce the lytic effect of microbial cells within them. Ion concentrations
of the aqueous-phase samples were measured using Dionex 600 ion
chromatography (Triad Scientific, Inc., Manasquan, NJ, USA) following the
manufacturer's instructions.

To obtain aqueous-phase DNA, the aqueous phase of each sample after oil and
water separation was first filtered through a 0.22 µm pore size
polycarbonate membrane filter and a portion of the membrane filter was used
to extract DNA with a AxyPrep™ Bacterial Genomic DNA Miniprep Kit
according to the manufacturer's instructions (Axygen Biosciences, USA). For the
oil phase, three volumes of iso-octane (2, 2, 4-trimethylpentane) were used
to dissolve the crude oil and then centrifuged at 5000 rpm for 30 min to
concentrate non-dissolved particulates and microbial cells at least
three times to obtain enough materials. Repetitive DNA extractions were then
conducted on these materials and combined to meet the quantity requirement
for the downstream quality control. Finally, all DNA samples from the aqueous
and oil phases were measured by the Nanodrop Model 2000 for concentration and checked for
DNA integrity by electrophoresis.

A FunGene pipeline chimera check was applied to check mcrA gene sequences using
the UCHIME de novo mode (Edgar et al., 2011). USEARCH software was used to
check chimeras of methanogenic 16S rRNA gene sequences using a QIIME-compatible SILVA 119 release SSURef database (“rdp_gold
fasta”) file as a reference. Then, for mcrA gene sequences, a de novo operational taxonomic unit (OTU) picking method
was applied by QIIME at a cutoff value of 0.05
(Caporaso et al., 2010). Representative OTU sequences
were aligned and inserted into the mcrA gene ARB project database through the maximum
parsimony method without changing the initial tree topology (Angel et
al., 2012; Ludwig et al., 2004). The phylogenetic affiliation was assigned,
and taxonomic composition results were processed by QIIME accordingly
(Fig. S1 in the Supplement). For methanogenic 16S rRNA gene sequence clustering and
diversity analysis, the same method was applied via QIIME as described in
the followings.

2.4 MiSeq sequencing and QIIME-based analysis

The prokaryotic universal primer pair 515F–909R (Caporaso et al., 2012;
Wang and Qian, 2009) and archaeal universal primer pair Arch347F–Arch806R
(Takai and Horikoshi, 2000) were used to amplify samples of
this study (both with barcodes attached to the forward primers, Table 2).
Two independent polymerase chain reaction (PCR) reactions were conducted and then combined to yield
enough PCR products to compromise variations between different batches.
Then, pooled PCR products of each sample with approximately 100 or 200 ng
DNA were subjected to one MiSeq run. Sequencing samples were prepared using
TruSeq DNA Kit according to the manufacturer's instruction. The library was
uploaded to an Illumina MiSeq platform for sequencing with reagent kit v2
(2× 250 bp) or v3 (2× 300 bp), as described in the
manufacturer's manual.

After merging paired-end reads from raw sequencing data with FLASH-1.2.8,
the fastx toolkit was applied to split merged reads from one run into individual
samples according to the primer barcodes (Magoc and Salzberg, 2011).
Then, all sequences were split into each library with the name of each
sample attached according to the barcode map using the QIIME command
“split_libraries” (Caporaso et al.,
2010). The criterion for filtering out underqualified sequences was “-s 15
-k -a 6 -r -l 150 -b 12 -M 5 -e 0”. Chimera checking was conducted by
USEARCH software using the QIIME-compatible SILVA 119 release SSURef database
(“rdp_gold” fasta) file as the reference (Edgar,
2010). Clustering; picking OTU; taxonomy assignment, aligning, filtering
alignments, and phylogenetic tree construction; taxonomic composition
summarizing; and alpha and beta diversity analyses were conducted step-by-step
by the QIIME pipeline with QIIME-compatible SILVA 119 SSURef database as the
reference (Caporaso et al., 2010). In clustering, the
“pick_open_reference_otus.py”
command was used to conduct OTU dividing, and the BLAST method was used to assign
taxonomy to input sequences. This subsampled open-reference OTU picking
method was the performed optimized and optimal strategy suggested by the
developers (Rideout et al., 2014). After OTU table “biom”
files were generated, in order to get the bacterial community composition
information from prokaryotic 16S rRNA gene primer amplified libraries, the
“filter_taxa_from_otu_table.py” command was introduced to only retain
bacterial OTUs in the “biom” file. Similarly, exclusive archaeal and
methanogenic OTU table files could also be processed from archaeal 16S rRNA
gene primer amplified libraries. Summary information of each sample OTU
abundance could be calculated by the “biom summarize-table” command, and then
the lowest number among all samples was chosen as the subsampling size to
make each library acquire an even size using the “multiple_rarefaction_even_depth.py” command. The
taxonomic compositional table was drawn according to the subsampled biom
file. Since there was no lane mask file available in this SILVA-compatible
119 release SSURef database, an alignment filtering method was performed
independently with an entropy threshold of 0.1 and gap filter threshold of 0.9
after obtaining aligned sequences via the PyNAST method. Diversity parameters of
each library could be generated by alpha diversity calling, with the
rarefaction curve; Good's coverage value; and Shannon, Chao1, Simpson, and phylogenetic diversity (PD)
whole tree indices calculated. Beta diversity, which delineates the
dissimilarity relationship among samples, was generated and visualized
through unweighted and weighted UniFrac matrix and non-phylogenetic
Bray–Curtis matrix method. The pairwise-shared OTU numbers were calculated
from “biom” by the command “shared_phylotypes.py”. Core
microbiome (shared OTU table in a specific sample category) was identified
by the “compute_core_microbiome.py” command.

2.5 Diversity and statistical analysis

The statistical significance of the community composition of samples among
different categories was valued by the anosim and adonis methods, implemented in
“compare_categories” command in QIIME. A Mantel test was used
to compare the distance matrix of physicochemical parameters and
UniFrac and Bray–Cutis distance matrix from beta diversity analysis by QIIME.
For aqueous-phase samples, both of the in situ physicochemical parameters and ion
concentrations were used in the analysis, while only in situ physicochemical
parameters were included for oil-phase samples. The compositional bar chart
and bubble chart were modified and illustrated from taxonomic summary
results. Tax4Fun was used to predict the functional capabilities based on
abundance profiles of microbial 16S rRNA gene datasets (Asshauer et
al., 2015). Linear discriminant analysis (LDA) effect size (LEfSe) analysis was applied to illustrate the
biomarker species with high statistical significance in different sample
categories and the functional profiles statistically distributed in
different sample categories (Segata et al., 2011).

2.6 Quantitative PCR on mcrA gene abundance

The quantitative PCR measurement was conducted using the iTaq™ Universal
SYBR® Green Supermix Kit (BIO-RAD). The qPCR
mixture contained in 15 µL : 7.5 µL of Supermix, 16 µg of bovine serum albumin (BSA)
(Roche), and 1 µM final concentration of the primer pair, i.e., ME3MF and ME3MF-e
(250 : 1) and ME2r′. Annealing temperature was set to be the same as the
clone library PCR setting, and the rest thermocycling settings were
according to the manufacturer's instructions. A single randomly picked pMD18-T
plasmid with the mcrA gene inserted was used to make the standard curve. The DNA
template concentration was adjusted to 0–40 ng µL−1. Results that
deviated significantly from values in the replicate groups were omitted, and
undetermined results (under the detection limit) were also deleted. The
property of the final adjusted standard curve is r2=0.995 and Eff% = 83.32.

3.1 Common OTU among different categories and core bacterial microbiome

Community composition results showed that 21 bacterial phyla were obtained
with an average abundance of more than 0.1 % (Fig. 1a) and that the three major
archaeal phyla were Thaumarchaeota, Euryarchaeota, and Crenarchaeota (Fig. 1b). Pairwise-shared OTU numbers of all
samples indicated that, irrespective of combinations between aqueous- and oil-phase samples, the average numbers of shared bacterial OTUs ranged from
199.9 to 292.4, accounting for 26.6 %–36.2 % of the total OTU numbers within
individual samples; average numbers of shared archaeal OTUs ranged from 1.8
to 11.9, accounting for 8.5 %–23.4 % of total OTU numbers within individual
samples (Table S4 in the Supplement). Core bacterial OTU numbers among aqueous, oil, and all
samples were 73, 57, and 46, which accounted for 7.1 %–10.1 %, 5.9 %–10.3 %,
and 4.5 %–8.3 % of OTU numbers in individual samples. The core archaeal OTU
number among aqueous samples was only 3, accounting for 3.9 %–8.1 % of OTU
numbers in individual samples, and no archaeal OTU was shared among oil-phase samples.

Figure 1Relative abundance of bacteria (a) and archaea (b) from 14 aqueous-
and oil-phase samples. Bacterial community was taxonomically assigned at the
level of phylum. Those phyla with an average abundance of all samples below
0.1 % were combined into the “other bacteria” category. The archaeal
community was taxonomically assigned at the level of class.

However, by investigating taxonomic profiles of core bacterial OTUs, the
shared OTUs were 49, 41, and 34 genera in aqueous, oil, and all samples,
corresponding to 65.5 %, 59.9 %, and 58.8 % of average sequences in the
total bacterial community, respectively (Tables S5 and S6). Most of the core
bacterial OTUs belonged to the most abundant 36 genera, of which the numbers
of shared genera among aqueous, oil, and all samples were 28, 23, and 23,
respectively (Fig. 2, and Tables S5 and S6).

There was no significant difference of shared bacterial OTU numbers within
and between aqueous- and oil-phase samples, suggesting a core microbiome was
shared among all components. The core OTUs covered around two-thirds of the total
bacterial sequences, even though the percentages of core or total OTU number
for individual samples were 4.5 % to 10.3 %. The core microbiome shared
among all petroleum reservoirs could be the key participants mediating
critical microbial processes, such as activation, degradation, fermentation,
oil emulsification, and methane generation (Yamane et al., 2008; Wang et
al., 2014; Pham et al., 2009; Orphan et al., 2000; Magot et al., 2000). This
spectrum of core microbiome shares common functional roles in facilitating
MEOR and is modified by the in situ physicochemical conditions of different
reservoirs (Fig. 2 and Table S5). It is important to connect the major
microbial players, including their community compositions and specific
functional capacities, to the interpretation of MEOR processes in the
petroleum reservoirs. Meanwhile, the core microbiome serves as a good basis
for simplifying microbial participants and, primarily, their roles in the petroleum
reservoirs and is useful for modeling and monitoring the MEOR
processes of petroleum reservoirs from different locations. Moreover,
substantial portions of the aerobic bacteria being discovered in the core
bacterial microbiome across different reservoirs imply that exogenous
bacteria introduced into subsurface reservoirs by water flushing can be also
represented in the core composition and it plays important roles in enhancing
oil recovery (Gao et al., 2015b; Youssef et al., 2009).

3.2 Shift of major methanogenesis pathways between the oil and aqueous phases

The methanogenic process generally shifts from the dominant hydrogenotrophic
pathway in the aqueous phase to the acetoclastic pathway in the oil phase of
high-temperature reservoirs. The opposite is true for low-temperature
samples, but no difference was detected between the two phases in moderate
temperature reservoirs (Fig. 3). The different patterns of shifting of the
major methanogenesis mechanisms in the aqueous and oil phases were evident in
different temperature petroleum reservoirs of this study. Quantitative
measurements of mcrA gene in both the aqueous and oil phases of all samples are
summarized in the Supplement (Fig. S1).

Figure 3Bubble chart of the proportional composition of methanogens based
on MiSeq sequencing results of archaeal 16S rRNA genes and clone sequencing
results of the mcrA gene and methanogenic 16S rRNA gene. The major substrate
utilization properties originated in Liu and Whitman (2008). MeNH2 is
methylamine and substrates in parentheses refer to being utilized by some
but not all species. Methanothermobacter thermautotrophicus strain Delta H is the phylogenetically closest
cultured clone to Methanobacteriaceae archaeon 15aZ. Thus, the combination of
Methanothermobacter and the Methanobacteriaceae archaeon 15aZ was used. Methanogenesis shift was based on the
transition of major methanogenesis pathway. Abbreviations: Aceto. M., Hydro.
M., and Methylo. M. are Acetoclastic methanogenesis, Hydrogenotrophic
methanogenesis, and Methylotrophic methanogenesis, respectively.

The underlying methanogenesis mechanism could change substantially in
reservoirs with distinctive geochemical properties (Magot et al.,
2000). The dominance of methylotrophic methanogenesis is rarely observed in
petroleum reservoirs. It is claimed that there is a very low possibility
that methyl-compounds could be generated during the degradation of kerogen
(Mesle et al., 2013). However, the dominance of obligate
methylotrophic Methanolobus was found in all water, rock and coal samples in a coal bed
methane reservoir within the eastern Ordos Basin of China (Guo et
al., 2012). It could be deduced that the prevalence of methylotrophic
methanogenesis under certain conditions is directly fueled by the existence
and availability of methyl-containing substances. Since the relative
abundances of bacteria in petroleum reservoir samples are always higher than
those of methanogenic community, the methanogenesis process might not be the
dominant process among all microbial processes. For instance, in P1 and P5
aqueous samples, sulfate concentrations are considerably high (Table 1) and
geochemical conditions are more favorable for sulfate reduction than
methanogenesis. A large proportion of Firmicutes and Deferribacteres in P5A
were potentially responsible for the activities of sulfate reduction,
according to their relative abundance (Fig. 1) and functional capacities
(Fig. 2). It is suggested the methylated compounds could be produced by
the degradation of glycine betaine (an osmoprotective agent) that are
accumulated or generated by halophiles in saline petroleum (Ollivier and
Alazard, 2010). Subsequently, it fuels the growth of methylotrophic
methanogens in certain petroleum environments. Although no molecular or
chemical evidence was reported for this process in petroleum environments, a
pioneer study on hydraulic fracturing in shales has detected glycine betaine
as the major known osmoprotectant in the produced fluids, and the combined
metagenomic and metabolite findings suggest the similar glycine betaine
utilization pathways in fractured shales (Daly et al., 2016).

Numerous studies based on in situ or in enrichment incubation microcosms on the aqueous
phase of reservoir fluids indicate that syntrophic acetate oxidation
associated with hydrogenotrophic methanogenesis is the major hydrocarbon
degradation pathway (Wang et al., 2012; Mayumi et al., 2011; Lee et al.,
2015; Mbadinga et al., 2012; Gray et al., 2011). The dominance of
hydrogenotrophic methanogenesis in subsurface ecosystems could result from
the external hydrogen originating from maturation of organic matter and/or
mineral hydrolysis (Head et al., 2003) and the synergistic
effect, in association with acetate oxidizers, whereby acetate was firstly
oxidized to H2 and CO2 is then utilized by methanogenesis
(Liu and Whitman, 2008). Additionally, a stable
isotope labeling experiment on oil-degrading microcosms showed that despite
the coexistence of acetoclastic methanogenesis and acetate syntrophic
oxidization in the initial state, the latter process prevailed over the
former one when introducing a low acetate concentration initially
(Gray et al., 2011). The above
evidence suggests that acetate syntrophic oxidization could exceed
acetoclastic methanogenesis and contribute substrates H2 and CO2
to potentially favor hydrogenotrophic methanogenesis process.

It is still difficult to determine whether the temperature has directly or
indirectly been involved in the alteration of the methanogenesis pathways.
Reservoir fluid constituents may affect methanogenic degradation because
crude oil and creosote inhibit acetoclastic methanogenesis (Warren
et al., 2004) and volatile hydrocarbons (nC5–nC10) inhibit
methanogenic degradation rates without changing the abundances of both
hydrogenotrophic and acetoclastic methanogens (Sherry et al.,
2014). Since most of the currently available community data are based on the
microbial assemblages within injection or production water, a new
understanding of the local microbiome distribution and changes should focus
on the oil or hydrophobic fraction of the reservoir fluids (Kobayashi et al.,
2012; Tang et al., 2012; Lenchi et al., 2013). Consequently, the alteration
pattern of major methanogenesis in the aqueous and oil phases under
different temperature conditions could be further delineated. A combination
of methods, including synthesis and quantification of the degradation
intermediate (Bian et al., 2015), stable isotope labeling on tracing
substrate transformation (Gray et
al., 2011), and molecular analysis of the metabolically active microorganisms
can advance the information of anaerobic degradation and methanogenesis
processes in reservoir systems.

3.3 Physicochemical influence and taxa and function profiles

Temperature is an important physical factor shaping the community structure
of bacterial (anosim and adonis p<0.01) and methanogenic
communities (anosim p<0.05 and adonis p<0.01) of the
samples in this study (Fig. 4 and Table S7). Furthermore, a significant
difference of taxa abundance among the three temperature categories for both
bacterial and archaeal communities was evident by LEfSe analysis (Fig. 5).
For the bacterial community, sample group (aqueous or oil phase from the same
sample group), temperature (anosim and adonis p<0.01), and pH
(adonis p<0.05) showed significant effects on separating samples
into different categories. For the archaeal community, significant differences
among sample categories were detected with sample group and temperature
(both adonis p<0.05) and pH and phase (anosim and adonis p<0.05). For the methanogenic community, significant differences among sample
categories were detected with sample group and temperature (sample group:
anosim and adonis p<0.05; temperature: anosim p<0.05 and
adonis p<0.01) (Table S7).

Figure 4Principal coordinate analysis plot figures based on unweighted
UniFrac matrices. Bacterial (a) and methanogenic (b) communities of 14
samples were separately analyzed to delineate the dissimilarity distances
between each sample based on phylogenetic classification. The sample dots
were categorized in terms of temperature (dot shape) and pH condition
(color).

Figure 5Cladogram based on LEfSe analysis results of bacteria (a, c) and
archaea (b, d) in terms of temperature (a, b) and pH (c, d) categories. The
taxonomic trees were generated from phylum to genus (inside to outside) in
the hierarchical structure. Biomarker taxonomic levels were labeled as the color
that had a logarithmic LDA score of at least 3.5. Pre-sample normalization
was used to format the relative abundance data. All-against-all strategy was
used in the multi-class analysis step.

The nitrate concentration dissimilarity matrix was significantly associated with
all unweighted and weighted UniFrac and Bray–Cutis matrices (all p<0.05) for aqueous bacterial community (Table S8). Meanwhile, methanogenic
community in the oil phase was significantly affected by reservoir depth,
temperature, pH, and water flooding operation years based on association
analysis of the weighted UniFrac matrix but not the unweighted UniFrac matrix (all
p<0.05) (Table S8), indicating that it was the abundance difference
of certain taxa affecting the compositional pattern. More detailed
relationships between physicochemical factors and
bacterial, archaeal, and methanogenic communities are summarized in the Supplement (Tables S7 and
S8).

The nitrate dissimilarity matrix was significantly correlated with dissimilarity
matrices of all aqueous-phase bacterial communities using both
unweighted and weighted UniFrac and Bray–Curtis matrices (all p<0.05)
(Table S8). Nitrate is an important chemical used in injection water to
inhibit corrosion and maintain crude oil quality (Gao et al., 2013).
Nitrate stimulates the growth of nitrate-reducing bacteria and inhibits the
growth of SRB (Nemati et al., 2001; Gao et al., 2013). Consequently,
nitrate injection shapes the microbial communities in petroleum reservoirs.
Mn2+ and Mg2+ were shown to be strongly associated with the
bacterial community of the aqueous phase based on the Bray–Curtis matrix (both p<0.05) (Table S8). Metal ions can be electron acceptors for direct
or indirect hydrocarbon degradation under anaerobic conditions
(Mbadinga et al., 2011). Metal reducers could also utilize
electrons from syntrophic partners to further facilitate direct aromatic
hydrocarbon degradation (Kunapuli et al., 2007).

Differences in taxa and function profiles in the oil and aqueous phases were
analyzed based on LEfSe and Tax4Fun (Figs. S2 and S3, and Table S9). For
bacterial communities, amino-acid metabolism and xenobiotics biodegradation
and metabolism were distributed more in the oil phase, while carbohydrate
metabolism was distributed more in the aqueous phase. For archaeal communities,
ubiquinone and other terpenoid quinone biosynthesis and butanoate and
tryptophan metabolism, together with other glycan degradation pathways were
distributed more in the oil phase. Since FTU (fraction of taxa that could be mapped to existing KEGG pathway) values of archaeal communities
were
unevenly distributed from sample to sample, the reliability of these
functional predictions is in question (Table S9). Meanwhile, the database of
Tax4Fun is far from completeness, due to the enormous amount of uncultured
microorganism and their unknown genomes. To date, there are still very
limited studies investigating the inhabiting preference of microbiomes in
petroleum reservoirs. Meanwhile, their functional contributions to
hydrocarbon degradation and methanogenesis in both the aqueous and oil phases
remain elusive, which calls for further efforts towards this point (Kryachko
et al., 2012; Wang et al., 2014; Kobayashi et al., 2012).

A core bacterial microbiome containing a small proportion of OTUs but a
relatively large proportion of sequences mediating hydrocarbon degradation
and fermentation was revealed by analysis of oil reservoirs of different
temperatures. The core and common bacterial microbiome of major biodegrading
functions were shared among geographically and physicochemically different
reservoirs. The different and shifting patterns of the dominant
methanogenesis pathway in the aqueous and oil phases within samples of different
temperatures were evident. Factors of pH; temperature; phase conditions; and
nitrate, Mn2+, and Mg2+ concentrations shaped the microbial
compositional and functional profiles significantly. Moreover, biomarker
groups of bacteria and archaea associated with different pH, temperature, and
phase conditions indicate major differences in the biochemical function of
amino acid metabolism, xenobiotics metabolism enriched in the oil phase, and
carbohydrate metabolism enriched in the aqueous phase.

ZZ, BL, LYW, BZM, HS, and JDG conceived the project and designed
the experiments. ZZ, BL, and LYW conducted the sampling, chemical, and molecular
experiments. JFL, BZM, and JDG managed sample collection and supervised
data interpretation. ZZ performed the original data analysis and drafted the
original manuscript. All members contributed to refining the manuscript and
approved the final version.

Xiangzhen Li's group at the Chengdu Institute of Biology, Chinese Academy of
Sciences, is thanked for their MiSeq sequencing efforts and related technical
support. Kelly Lau is thanked for her supportive technician work. We
are grateful for the support from local administrative and technical staff at the Shengli,
Daqing, Huabei, Xinjiang Karamay, and Jiangsu oilfields.

This study shows a core bacterial microbiome with a small proportion of shared operational taxonomic units of common sequences among all oil reservoirs. Dominant methanogenesis shifts from the hydrogenotrophic pathway in water phase to the acetoclastic pathway in the oil phase at high temperatures, but the opposite is true at low temperatures. There are also major functional metabolism differences between the two phases for amino acids, hydrocarbons, and carbohydrates.

This study shows a core bacterial microbiome with a small proportion of shared operational...